Abstract
Collaborative filtering-based recommender systems are vulnerable to shilling attacks. How to detect shilling attacks has become a popular research direction. Some recent works have applied deep learning to the field of shilling attack detection. However, most of the existing deep learning-based shilling attack detection models are based on user-item scoring matrices, which do not apply manual scoring features well and cannot be used to detect cold-start shilling attackers. Thus, we propose a shilling attack detection algorithm based on Supervised Prototypical Variational Auto-Encoder (SP-VAE). Specially, SP-VAE can obtain a unified user-profile representation that can be easily used to down-stream applications of shilling attack detection classifiers. Then, the algorithm constructs the prototype representation of various shilling attacker, and a classifier is used to classify various shilling attack users and normal users. The experimental results show that our method consistently outperforms the traditional method in the case of cold-start profile of the shilling attack.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant Nos. 72172057, 71701089, 92046026, in part by the Fundamental Research on Advanced Leading Technology Project of Jiangsu Province under Grant BK20192004C, BK20202011.
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Wang, X., Zhao, H., Wang, Y., Tao, H., Cao, J. (2022). Supervised Prototypical Variational Autoencoder for Shilling Attack Detection in Recommender Systems. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2022. Communications in Computer and Information Science, vol 1745. Springer, Singapore. https://doi.org/10.1007/978-981-19-8991-9_17
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DOI: https://doi.org/10.1007/978-981-19-8991-9_17
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